Underwater multidimensional metrology in degraded environments with augmented reality devices

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Abstract
We propose an underwater multidimensional metrology approach in degraded environments utilizing augmented reality devices. The approach uses a risk-aware next-best-view (NBV) framework for metrology in degraded visual environments, introducing a conditional value-at-risk (CVaR) objective to guide viewpoint selection. Using anisotropy-aware geodesic metrics, local sensitivities, and a CVaR optimization, the approach prioritizes views that reduce worst-case geodesic-length uncertainty. We validated the performance of the proposed system experimentally on turbid scenes with augmented reality device imagery and five-dimensional WaterSplat reconstructions. The results across increasing turbidity demonstrate that CVaR achieves 2 − 3 × greater median error reduction compared to entropy-based NBV selection. We further conduct controlled synthetic experiments to analyze asymptotic error plateaus. To the best of our knowledge, this is the first report on an augmented reality-based NBV approach aimed at improving metrology in turbid environments.
Funding Information
  • Office of Naval Research (N000142212375)
  • Office of Naval Research (N000142212349)
  • United States Air Force Office of Scientific Research (FA9550-24-1-0128)

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